Architecture Overview

Code-Graph-RAG consists of two main components that work together to analyse and query codebases.

Components

1. Multi-Language Parser

A Tree-sitter based parsing system that analyses codebases and ingests data into Memgraph.

• Uses Tree-sitter for robust, language-agnostic AST parsing
• Extracts functions, classes, methods, modules, and their relationships
• Supports 13 programming languages with a unified graph schema (plus Scala in development)
• Handles complex patterns like nested functions, class hierarchies, and cross-module calls

2. RAG System (codebase_rag/)

An interactive CLI for querying the stored knowledge graph.

• Translates natural language questions into Cypher queries
• Retrieves source code snippets for found elements
• Supports AI-powered code editing with AST-based targeting
• Provides code optimisation with interactive approval workflow

Data Flow

Source Code → Tree-sitter Parser → AST Analysis → Memgraph Knowledge Graph
                                                          ↓
User Query → AI Model (Cypher Gen) → Cypher Query → Graph Results → Response

Key Dependencies

Dependency	Purpose
tree-sitter	Language-agnostic AST parsing
pymgclient	Memgraph database adapter
pydantic-ai	Agent framework for LLM integration
pydantic-settings	Settings management
mcp	Model Context Protocol SDK
typer	CLI framework
rich	Terminal rendering
prompt-toolkit	Interactive command line
diff-match-patch	Code patching
watchdog	Filesystem events monitoring
huggingface-hub	UniXcoder model download